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AgentPeerTalk: Empowering Students through Agentic-AI-Driven Discernment of Bullying and Joking in Peer Interactions in Schools

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arxiv 2408.01459 v1 pith:5BPO7YRX submitted 2024-07-27 cs.CY cs.AIcs.CL

Aditya Paul , Chi Lok Yu , Eva Adelina Susanto , Nicholas Wai Long Lau , Gwenyth Isobel Meadows This is my paper · ORCID
classification cs.CYcs.AIcs.CL
keywords bullyingstudentsagenticchatgpt-4approachinteractionsjokingllms
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Addressing school bullying effectively and promptly is crucial for the mental health of students. This study examined the potential of large language models (LLMs) to empower students by discerning between bullying and joking in school peer interactions. We employed ChatGPT-4, Gemini 1.5 Pro, and Claude 3 Opus, evaluating their effectiveness through human review. Our results revealed that not all LLMs were suitable for an agentic approach, with ChatGPT-4 showing the most promise. We observed variations in LLM outputs, possibly influenced by political overcorrectness, context window limitations, and pre-existing bias in their training data. ChatGPT-4 excelled in context-specific accuracy after implementing the agentic approach, highlighting its potential to provide continuous, real-time support to vulnerable students. This study underlines the significant social impact of using agentic AI in educational settings, offering a new avenue for reducing the negative consequences of bullying and enhancing student well-being.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generative to Agentic AI: Survey, Conceptualization, and Challenges

    cs.AI 2025-04 conditional novelty 4.0 of 10

    Agentic AI is characterized over Generative AI by iterative reasoning, environment interaction, memory, and tool use, with autonomy as the defining difference.

  2. Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions

    cs.CL 2025-05 reject novelty 2.0 of 10

    A survey of LLM-based hypothesis generation and validation whose taxonomy is useful in outline but whose citations and tool descriptions are unreliable.

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